Abstract
the published version if accessible, as it contains editor’s improvements. (c) 2011 IEEE.∗ In the Python world, NumPy arrays are the stan-dard representation for numerical data. Here, we show how these arrays enable efficient implemen-tation of numerical computations in a high-level language. Overall, three techniques are applied to improve performance: vectorizing calculations, avoiding copying data in memory, and minimizing operation counts. We first present the NumPy array structure, then show how to use it for efficient computation, and finally how to share array data with other li-braries.
Keywords
Python (programming language)ComputationCopyingComputer scienceData structureComputational scienceParallel computingArray data structureAlgorithmProgramming language
Affiliated Institutions
Related Publications
MDAnalysis: A toolkit for the analysis of molecular dynamics simulations
Abstract MDAnalysis is an object‐oriented library for structural and temporal analysis of molecular dynamics (MD) simulation trajectories and individual protein structures. It i...
<i>PHENIX</i>: a comprehensive Python-based system for macromolecular structure solution
Macromolecular X-ray crystallography is routinely applied to understand biological processes at a molecular level. However, significant time and effort are still required to sol...
Actors: A Model of Concurrent Computation in Distributed Systems
A foundational model of concurrency is developed in this thesis. It examines issues in the design of parallel systems and show why the actor model is suitable for exploiting lar...
Synchronization in actor systems
This paper presents a mechanism for the arbitration of parallel requests to shared resources. This mechanism is the serialize, which may be described as a kind of protection mec...
A fast, lock-free approach for efficient parallel counting of occurrences of <i>k</i> -mers
Abstract Motivation: Counting the number of occurrences of every k-mer (substring of length k) in a long string is a central subproblem in many applications, including genome as...
Publication Info
- Year
- 2011
- Type
- article
- Volume
- 13
- Issue
- 2
- Pages
- 22-30
- Citations
- 10556
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
10556
OpenAlex
470
Influential
8662
CrossRef
Cite This
Stéfan van der Walt,
Steven C. Colbert,
Gaël Varoquaux
(2011).
The NumPy Array: A Structure for Efficient Numerical Computation.
Computing in Science & Engineering
, 13
(2)
, 22-30.
https://doi.org/10.1109/mcse.2011.37
Identifiers
- DOI
- 10.1109/mcse.2011.37
- arXiv
- 1102.1523